Session-based recommendation aims at predicting the next item given a sequence of previous items consumed in the session, e.g., on e-commerce or multimedia streaming services. Specifically, session data exhibits some unique characteristics, i.e., session consistency and sequential dependency over items within the session, repeated item consumption, and session timeliness. In this paper, we propose simple-yet-effective linear models for considering the holistic aspects of the sessions. The comprehensive nature of our models helps improve the quality of session-based recommendation. More importantly, it provides a generalized framework for reflecting different perspectives of session data. Furthermore, since our models can be solved by closed-form solutions, they are highly scalable. Experimental results demonstrate that the proposed linear models show competitive or state-of-the-art performance in various metrics on several real-world datasets.
|Title of host publication||The Web Conference 2021 - Proceedings of the World Wide Web Conference, WWW 2021|
|Publisher||Association for Computing Machinery, Inc|
|Number of pages||12|
|Publication status||Published - 2021 Apr 19|
|Event||2021 World Wide Web Conference, WWW 2021 - Ljubljana, Slovenia|
Duration: 2021 Apr 19 → 2021 Apr 23
|Name||The Web Conference 2021 - Proceedings of the World Wide Web Conference, WWW 2021|
|Conference||2021 World Wide Web Conference, WWW 2021|
|Period||21/4/19 → 21/4/23|
Bibliographical noteFunding Information:
This work was supported by the National Research Foundation of Korea (NRF) (NRF-2018R1A5A1060031). Also, this work was supported by Institute of Information & communications Technology Planning & evaluation (IITP) grant funded by the Korea government (MSIT) (No.2019-0-00421, AI Graduate School Support Program and IITP-2020-0-01821, ICT Creative Consilience Program).
Â© 2021 ACM.
All Science Journal Classification (ASJC) codes
- Computer Networks and Communications